Overview

Dataset statistics

Number of variables12
Number of observations525370
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.7 MiB
Average record size in memory268.8 B

Variable types

Text1
DateTime1
Numeric9
Categorical1

Alerts

open has 194199 (37.0%) zerosZeros
high has 194199 (37.0%) zerosZeros
low has 194199 (37.0%) zerosZeros
volume has 193526 (36.8%) zerosZeros
open_interest has 82449 (15.7%) zerosZeros
turnover has 193526 (36.8%) zerosZeros

Reproduction

Analysis started2024-03-21 02:20:54.147280
Analysis finished2024-03-21 02:21:25.034900
Duration30.89 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

symbol
Text

Distinct2307
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size31.3 MiB
2024-03-21T10:21:26.143002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4161486
Min length5

Characters and Unicode

Total characters2845482
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa1101
2nd rowa1103
3rd rowa1105
4th rowa1107
5th rowa1109
ValueCountFrequency (%)
a1609 373
 
0.1%
a1809 372
 
0.1%
a1709 371
 
0.1%
a1509 371
 
0.1%
a1209 371
 
0.1%
a1501 370
 
0.1%
a1601 370
 
0.1%
a1701 369
 
0.1%
a1909 369
 
0.1%
a1801 368
 
0.1%
Other values (2297) 521666
99.3%
2024-03-21T10:21:27.462929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 573335
20.1%
0 481015
16.9%
2 390346
13.7%
b 130302
 
4.6%
3 126409
 
4.4%
p 107891
 
3.8%
4 99895
 
3.5%
j 98039
 
3.4%
9 94592
 
3.3%
5 91304
 
3.2%
Other values (17) 652354
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2845482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 573335
20.1%
0 481015
16.9%
2 390346
13.7%
b 130302
 
4.6%
3 126409
 
4.4%
p 107891
 
3.8%
4 99895
 
3.5%
j 98039
 
3.4%
9 94592
 
3.3%
5 91304
 
3.2%
Other values (17) 652354
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2845482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 573335
20.1%
0 481015
16.9%
2 390346
13.7%
b 130302
 
4.6%
3 126409
 
4.4%
p 107891
 
3.8%
4 99895
 
3.5%
j 98039
 
3.4%
9 94592
 
3.3%
5 91304
 
3.2%
Other values (17) 652354
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2845482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 573335
20.1%
0 481015
16.9%
2 390346
13.7%
b 130302
 
4.6%
3 126409
 
4.4%
p 107891
 
3.8%
4 99895
 
3.5%
j 98039
 
3.4%
9 94592
 
3.3%
5 91304
 
3.2%
Other values (17) 652354
22.9%

date
Date

Distinct3203
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
Minimum2011-01-04 00:00:00
Maximum2024-03-11 00:00:00
2024-03-21T10:21:27.662177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:27.864857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

ZEROS 

Distinct17452
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2910.7923
Minimum0
Maximum29600
Zeros194199
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:28.048417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2010
Q34680
95-th percentile8901
Maximum29600
Range29600
Interquartile range (IQR)4680

Descriptive statistics

Standard deviation3336.5957
Coefficient of variation (CV)1.1462844
Kurtosis2.9666818
Mean2910.7923
Median Absolute Deviation (MAD)2010
Skewness1.3772142
Sum1.5292429 × 109
Variance11132871
MonotonicityNot monotonic
2024-03-21T10:21:28.240749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194199
37.0%
3500 224
 
< 0.1%
3400 196
 
< 0.1%
3700 195
 
< 0.1%
4300 194
 
< 0.1%
4700 190
 
< 0.1%
3800 188
 
< 0.1%
4400 188
 
< 0.1%
3600 188
 
< 0.1%
8300 188
 
< 0.1%
Other values (17442) 329420
62.7%
ValueCountFrequency (%)
0 194199
37.0%
39.5 1
 
< 0.1%
40.5 1
 
< 0.1%
40.75 2
 
< 0.1%
41 1
 
< 0.1%
41.1 1
 
< 0.1%
41.2 1
 
< 0.1%
41.7 1
 
< 0.1%
41.9 1
 
< 0.1%
42 4
 
< 0.1%
ValueCountFrequency (%)
29600 1
< 0.1%
29500 1
< 0.1%
29430 1
< 0.1%
29350 1
< 0.1%
29150 1
< 0.1%
29000 1
< 0.1%
28945 1
< 0.1%
28880 1
< 0.1%
28865 1
< 0.1%
28800 1
< 0.1%

high
Real number (ℝ)

ZEROS 

Distinct17900
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2933.7236
Minimum0
Maximum30680
Zeros194199
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:28.432099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2028
Q34710
95-th percentile8985
Maximum30680
Range30680
Interquartile range (IQR)4710

Descriptive statistics

Standard deviation3363.8506
Coefficient of variation (CV)1.1466147
Kurtosis2.9918832
Mean2933.7236
Median Absolute Deviation (MAD)2028
Skewness1.3807616
Sum1.5412903 × 109
Variance11315491
MonotonicityNot monotonic
2024-03-21T10:21:28.622030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194199
37.0%
3500 188
 
< 0.1%
3450 167
 
< 0.1%
3600 167
 
< 0.1%
3400 154
 
< 0.1%
3550 151
 
< 0.1%
4800 150
 
< 0.1%
4350 147
 
< 0.1%
4000 146
 
< 0.1%
3800 144
 
< 0.1%
Other values (17890) 329757
62.8%
ValueCountFrequency (%)
0 194199
37.0%
39.5 1
 
< 0.1%
40.6 1
 
< 0.1%
40.75 2
 
< 0.1%
42 2
 
< 0.1%
42.05 1
 
< 0.1%
42.1 1
 
< 0.1%
42.4 1
 
< 0.1%
42.9 1
 
< 0.1%
42.95 1
 
< 0.1%
ValueCountFrequency (%)
30680 1
< 0.1%
29805 1
< 0.1%
29435 1
< 0.1%
29430 1
< 0.1%
29380 1
< 0.1%
29325 1
< 0.1%
29250 1
< 0.1%
29140 1
< 0.1%
29115 1
< 0.1%
29000 1
< 0.1%

low
Real number (ℝ)

ZEROS 

Distinct17467
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2887.6408
Minimum0
Maximum28800
Zeros194199
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:28.811344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1994
Q34650
95-th percentile8824
Maximum28800
Range28800
Interquartile range (IQR)4650

Descriptive statistics

Standard deviation3308.5896
Coefficient of variation (CV)1.145776
Kurtosis2.9289193
Mean2887.6408
Median Absolute Deviation (MAD)1994
Skewness1.3719854
Sum1.5170799 × 109
Variance10946765
MonotonicityNot monotonic
2024-03-21T10:21:29.003797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 194199
37.0%
3400 176
 
< 0.1%
3450 173
 
< 0.1%
3500 168
 
< 0.1%
3650 162
 
< 0.1%
4300 160
 
< 0.1%
3600 160
 
< 0.1%
3700 158
 
< 0.1%
3550 156
 
< 0.1%
4700 155
 
< 0.1%
Other values (17457) 329703
62.8%
ValueCountFrequency (%)
0 194199
37.0%
39.5 1
 
< 0.1%
40.5 1
 
< 0.1%
40.75 2
 
< 0.1%
41 2
 
< 0.1%
41.1 1
 
< 0.1%
41.15 1
 
< 0.1%
41.3 1
 
< 0.1%
41.4 1
 
< 0.1%
41.7 1
 
< 0.1%
ValueCountFrequency (%)
28800 1
< 0.1%
28630 1
< 0.1%
28600 1
< 0.1%
28550 1
< 0.1%
28510 1
< 0.1%
28500 1
< 0.1%
28485 1
< 0.1%
28440 1
< 0.1%
28400 1
< 0.1%
28365 1
< 0.1%

close
Real number (ℝ)

Distinct19029
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4320.8067
Minimum37
Maximum29380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:29.187703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile122.7
Q11719
median3844
Q36465
95-th percentile9575
Maximum29380
Range29343
Interquartile range (IQR)4746

Descriptive statistics

Standard deviation3189.9395
Coefficient of variation (CV)0.73827405
Kurtosis2.1882776
Mean4320.8067
Median Absolute Deviation (MAD)2381.5
Skewness0.97052292
Sum2.2700222 × 109
Variance10175714
MonotonicityNot monotonic
2024-03-21T10:21:29.397408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 4988
 
0.9%
373.3 1208
 
0.2%
410.6 796
 
0.2%
377.9 725
 
0.1%
165.6 631
 
0.1%
351.8 528
 
0.1%
87.05 526
 
0.1%
92 335
 
0.1%
124.75 324
 
0.1%
3390 278
 
0.1%
Other values (19019) 515031
98.0%
ValueCountFrequency (%)
37 5
< 0.1%
37.4 5
< 0.1%
37.65 1
 
< 0.1%
38.05 1
 
< 0.1%
38.9 4
< 0.1%
39.3 4
< 0.1%
39.5 3
< 0.1%
39.75 1
 
< 0.1%
39.9 2
 
< 0.1%
40.15 1
 
< 0.1%
ValueCountFrequency (%)
29380 1
< 0.1%
29185 1
< 0.1%
28980 1
< 0.1%
28970 1
< 0.1%
28915 1
< 0.1%
28900 1
< 0.1%
28845 1
< 0.1%
28785 1
< 0.1%
28750 2
< 0.1%
28610 1
< 0.1%

volume
Real number (ℝ)

ZEROS 

Distinct87904
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33105.351
Minimum0
Maximum3825808
Zeros193526
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:29.588992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q34565
95-th percentile181152.1
Maximum3825808
Range3825808
Interquartile range (IQR)4565

Descriptive statistics

Standard deviation133121.83
Coefficient of variation (CV)4.0211574
Kurtosis86.203849
Mean33105.351
Median Absolute Deviation (MAD)10
Skewness7.6337436
Sum1.7392558 × 1010
Variance1.772142 × 1010
MonotonicityNot monotonic
2024-03-21T10:21:29.800237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193526
36.8%
1 18003
 
3.4%
2 13323
 
2.5%
3 8373
 
1.6%
4 6693
 
1.3%
5 5324
 
1.0%
6 4585
 
0.9%
7 3742
 
0.7%
8 3348
 
0.6%
10 2888
 
0.5%
Other values (87894) 265565
50.5%
ValueCountFrequency (%)
0 193526
36.8%
1 18003
 
3.4%
2 13323
 
2.5%
3 8373
 
1.6%
4 6693
 
1.3%
5 5324
 
1.0%
6 4585
 
0.9%
7 3742
 
0.7%
8 3348
 
0.6%
9 2882
 
0.5%
ValueCountFrequency (%)
3825808 1
< 0.1%
3763366 1
< 0.1%
3762910 1
< 0.1%
3729249 1
< 0.1%
3709767 1
< 0.1%
3707160 1
< 0.1%
3681688 1
< 0.1%
3596174 1
< 0.1%
3486760 1
< 0.1%
3478432 1
< 0.1%

open_interest
Real number (ℝ)

ZEROS 

Distinct107247
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38927.294
Minimum0
Maximum2260091
Zeros82449
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:30.008560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median95
Q311181
95-th percentile214095.8
Maximum2260091
Range2260091
Interquartile range (IQR)11179

Descriptive statistics

Standard deviation130230.09
Coefficient of variation (CV)3.3454699
Kurtosis50.761266
Mean38927.294
Median Absolute Deviation (MAD)95
Skewness6.2325508
Sum2.0451233 × 1010
Variance1.6959877 × 1010
MonotonicityNot monotonic
2024-03-21T10:21:30.194342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82449
 
15.7%
1 28698
 
5.5%
2 20968
 
4.0%
3 16968
 
3.2%
4 11793
 
2.2%
5 9167
 
1.7%
6 7430
 
1.4%
7 6109
 
1.2%
8 4610
 
0.9%
10 4255
 
0.8%
Other values (107237) 332923
63.4%
ValueCountFrequency (%)
0 82449
15.7%
1 28698
 
5.5%
2 20968
 
4.0%
3 16968
 
3.2%
4 11793
 
2.2%
5 9167
 
1.7%
6 7430
 
1.4%
7 6109
 
1.2%
8 4610
 
0.9%
9 4191
 
0.8%
ValueCountFrequency (%)
2260091 1
< 0.1%
2240622 1
< 0.1%
2232052 1
< 0.1%
2231971 1
< 0.1%
2224590 1
< 0.1%
2217014 1
< 0.1%
2203099 1
< 0.1%
2191507 1
< 0.1%
2190623 1
< 0.1%
2178391 1
< 0.1%

turnover
Real number (ℝ)

ZEROS 

Distinct238483
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174612.98
Minimum0
Maximum18662757
Zeros193526
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:30.385437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52.04
Q322782.982
95-th percentile905675.34
Maximum18662757
Range18662757
Interquartile range (IQR)22782.982

Descriptive statistics

Standard deviation747185.69
Coefficient of variation (CV)4.2790959
Kurtosis80.681771
Mean174612.98
Median Absolute Deviation (MAD)52.04
Skewness7.8539036
Sum9.1736421 × 1010
Variance5.5828645 × 1011
MonotonicityNot monotonic
2024-03-21T10:21:30.584488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193526
36.8%
4 86
 
< 0.1%
3.7 86
 
< 0.1%
4.8 81
 
< 0.1%
4.08 78
 
< 0.1%
3.6 77
 
< 0.1%
4.1 77
 
< 0.1%
4.5 74
 
< 0.1%
4.3 74
 
< 0.1%
3.9 73
 
< 0.1%
Other values (238473) 331138
63.0%
ValueCountFrequency (%)
0 193526
36.8%
1.05 1
 
< 0.1%
1.1 1
 
< 0.1%
1.11 2
 
< 0.1%
1.12 1
 
< 0.1%
1.13 1
 
< 0.1%
1.15 3
 
< 0.1%
1.16 3
 
< 0.1%
1.17 1
 
< 0.1%
1.18 1
 
< 0.1%
ValueCountFrequency (%)
18662757.2 1
< 0.1%
17996196.01 1
< 0.1%
17441620.56 1
< 0.1%
16742168.88 1
< 0.1%
16296677.41 1
< 0.1%
16284149.06 1
< 0.1%
16189322.05 1
< 0.1%
16105202.9 1
< 0.1%
16069741.18 1
< 0.1%
16032551.16 1
< 0.1%

settle
Real number (ℝ)

Distinct19117
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4320.7291
Minimum37
Maximum29100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:30.784071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile122.7
Q11718
median3844
Q36465
95-th percentile9571.55
Maximum29100
Range29063
Interquartile range (IQR)4747

Descriptive statistics

Standard deviation3190.0913
Coefficient of variation (CV)0.73832245
Kurtosis2.1914095
Mean4320.7291
Median Absolute Deviation (MAD)2381
Skewness0.97124402
Sum2.2699815 × 109
Variance10176683
MonotonicityNot monotonic
2024-03-21T10:21:31.006303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 4980
 
0.9%
373.3 1208
 
0.2%
410.6 797
 
0.2%
377.9 725
 
0.1%
165.6 632
 
0.1%
351.8 528
 
0.1%
87.05 525
 
0.1%
92 335
 
0.1%
124.75 325
 
0.1%
3390 240
 
< 0.1%
Other values (19107) 515075
98.0%
ValueCountFrequency (%)
37 5
< 0.1%
37.4 5
< 0.1%
37.65 1
 
< 0.1%
38.05 1
 
< 0.1%
38.9 4
< 0.1%
39.3 4
< 0.1%
39.5 3
< 0.1%
39.75 1
 
< 0.1%
39.9 2
 
< 0.1%
40.15 1
 
< 0.1%
ValueCountFrequency (%)
29100 1
< 0.1%
28985 1
< 0.1%
28910 1
< 0.1%
28875 1
< 0.1%
28860 1
< 0.1%
28830 1
< 0.1%
28765 1
< 0.1%
28755 1
< 0.1%
28700 1
< 0.1%
28635 1
< 0.1%

pre_settle
Real number (ℝ)

Distinct19105
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4320.6605
Minimum37
Maximum30680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-03-21T10:21:31.227024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile122.7
Q11718
median3844
Q36465
95-th percentile9574
Maximum30680
Range30643
Interquartile range (IQR)4747

Descriptive statistics

Standard deviation3190.8033
Coefficient of variation (CV)0.73849897
Kurtosis2.2138081
Mean4320.6605
Median Absolute Deviation (MAD)2382
Skewness0.97400343
Sum2.2699454 × 109
Variance10181226
MonotonicityNot monotonic
2024-03-21T10:21:31.421059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 4969
 
0.9%
373.3 1213
 
0.2%
410.6 800
 
0.2%
377.9 726
 
0.1%
165.6 635
 
0.1%
351.8 530
 
0.1%
87.05 529
 
0.1%
92 335
 
0.1%
124.75 325
 
0.1%
3390 239
 
< 0.1%
Other values (19095) 515069
98.0%
ValueCountFrequency (%)
37 5
< 0.1%
37.4 5
< 0.1%
37.65 1
 
< 0.1%
38.05 1
 
< 0.1%
38.9 4
< 0.1%
39.3 4
< 0.1%
39.5 3
< 0.1%
39.75 1
 
< 0.1%
39.9 2
 
< 0.1%
40.15 1
 
< 0.1%
ValueCountFrequency (%)
30680 1
< 0.1%
29680 1
< 0.1%
29100 1
< 0.1%
28985 1
< 0.1%
28910 1
< 0.1%
28875 1
< 0.1%
28860 1
< 0.1%
28830 1
< 0.1%
28765 1
< 0.1%
28755 1
< 0.1%

variety
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
L
38436 
P
38436 
V
38436 
J
37434 
JM
 
31886
Other values (16)
340742 

Length

Max length2
Median length1
Mean length1.4161486
Min length1

Characters and Unicode

Total characters744002
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
L 38436
 
7.3%
P 38436
 
7.3%
V 38436
 
7.3%
J 37434
 
7.1%
JM 31886
 
6.1%
I 30162
 
5.7%
BB 29804
 
5.7%
FB 29764
 
5.7%
PP 29261
 
5.6%
JD 28719
 
5.5%
Other values (11) 193032
36.7%

Length

2024-03-21T10:21:31.605612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l 38436
 
7.3%
p 38436
 
7.3%
v 38436
 
7.3%
j 37434
 
7.1%
jm 31886
 
6.1%
i 30162
 
5.7%
bb 29804
 
5.7%
fb 29764
 
5.7%
pp 29261
 
5.6%
jd 28719
 
5.5%
Other values (11) 193032
36.7%

Most occurring characters

ValueCountFrequency (%)
B 130302
17.5%
P 107891
14.5%
J 98039
13.2%
M 57510
 
7.7%
L 42764
 
5.7%
V 38436
 
5.2%
C 32651
 
4.4%
I 30162
 
4.1%
F 29764
 
4.0%
D 28719
 
3.9%
Other values (7) 147764
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 130302
17.5%
P 107891
14.5%
J 98039
13.2%
M 57510
 
7.7%
L 42764
 
5.7%
V 38436
 
5.2%
C 32651
 
4.4%
I 30162
 
4.1%
F 29764
 
4.0%
D 28719
 
3.9%
Other values (7) 147764
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 130302
17.5%
P 107891
14.5%
J 98039
13.2%
M 57510
 
7.7%
L 42764
 
5.7%
V 38436
 
5.2%
C 32651
 
4.4%
I 30162
 
4.1%
F 29764
 
4.0%
D 28719
 
3.9%
Other values (7) 147764
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 130302
17.5%
P 107891
14.5%
J 98039
13.2%
M 57510
 
7.7%
L 42764
 
5.7%
V 38436
 
5.2%
C 32651
 
4.4%
I 30162
 
4.1%
F 29764
 
4.0%
D 28719
 
3.9%
Other values (7) 147764
19.9%

Interactions

2024-03-21T10:21:20.977108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:05.127395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:07.055250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:09.209427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:11.137702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:13.030390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:15.146446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:17.031360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:18.981431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:21.207865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:05.336794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:07.257457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:09.454135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:11.341521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:13.253789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:15.340574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:17.249152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:19.179003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:21.437165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:05.531608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:07.481716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:09.657270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:11.544756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:13.483414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:15.560725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:17.455096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:19.411993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:21.662313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:05.726145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:07.694337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:09.865913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:11.743525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:13.712404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:15.764050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:17.678182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:19.662773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:21.872579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:05.964756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:07.892594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:10.066849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:11.966568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:13.926494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:15.975807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:17.909411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:19.888396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:22.122734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:06.193552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:08.365430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:10.304030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:12.190978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:14.155473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:16.190203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:18.130035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:20.145282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:22.346275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:06.401811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:08.576374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:10.502584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:12.382380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:14.382757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:16.397983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:18.331342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:20.369470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:22.562834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:06.617184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:08.794082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:10.711201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:12.592932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:14.681957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:16.615494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:18.553438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:20.569615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:22.993814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:06.844597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:09.006737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:10.912661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:12.819701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:14.930821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:16.819520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:18.762578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:21:20.772434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-21T10:21:23.331440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:21:23.868193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevariety
0a11012011-01-04 00:00:003993.04120.03993.04120.053.074.0213.084020.04040.0A
1a11032011-01-04 00:00:004110.04135.04110.04128.05.0108.020.624123.04117.0A
2a11052011-01-04 00:00:004355.04382.04355.04367.01104.08766.04827.654372.04362.0A
3a11072011-01-04 00:00:004460.04460.04444.04444.03.0372.013.354451.04449.0A
4a11092011-01-04 00:00:004528.04545.04520.04537.055262.0103325.0250617.194535.04531.0A
5a11112011-01-04 00:00:000.00.00.04521.00.0120.00.004521.04521.0A
6a12012011-01-04 00:00:004491.04534.04491.04524.041293.0112097.0186502.884516.04502.0A
7a12032011-01-04 00:00:000.00.00.04537.00.058.00.004537.04537.0A
8a12052011-01-04 00:00:004599.04648.04598.04634.010.0229.046.284627.04619.0A
9b11012011-01-04 00:00:000.00.00.04621.00.00.00.004621.04621.0B
symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevariety
525360v25012024-03-11 00:00:006110.06140.06055.06088.02831.06661.08612.566084.06104.0V
525361v25022024-03-11 00:00:006120.06130.06070.06090.015.0300.045.686091.06121.0V
525362y24032024-03-11 00:00:000.00.00.07502.00.0625.00.007502.07502.0Y
525363y24052024-03-11 00:00:007606.07650.07586.07614.0390687.0580930.02974332.127612.07584.0Y
525364y24072024-03-11 00:00:007524.07568.07512.07522.04676.016432.035209.407528.07536.0Y
525365y24082024-03-11 00:00:007520.07546.07492.07504.02091.06102.015706.557510.07522.0Y
525366y24092024-03-11 00:00:007498.07514.07460.07470.0116484.0228904.0872192.007486.07486.0Y
525367y24112024-03-11 00:00:007468.07486.07434.07456.01137.07262.08488.467464.07468.0Y
525368y24122024-03-11 00:00:007514.07558.07478.07534.0149.01692.01121.437526.07524.0Y
525369y25012024-03-11 00:00:007488.07534.07444.07494.07490.011201.056182.867500.07478.0Y